Measure Effectiveness of Virtual Training in Real Time with AWS AI Services
As per International Data Corporation (IDC), worldwide spending on digital transformation will reach $2.3 trillion in 2023. As organizations adopt digital transformation, training becomes an important aspect of this journey. Whether these are internal trainings to upskill existing workforce or a packaged content for commercial use, these trainings need to be efficient and cost effective. With the advent of education technology, it is a common practice to deliver trainings via digital platforms. This makes it accessible for larger population and is cost effective, but it is important that the trainings are interactive and effective. According to a recent article published by Forbes, immersive education and data driven insights are among the top five Education Technology (EdTech) innovations. These are the key characteristics of creating an effective training experience.
An earlier blog series explored how to build a virtual trainer on AWS using Amazon Sumerian. This series illustrated how to easily build an immersive and highly engaging virtual training experience without needing additional devices or a complex virtual reality platform management. These trainings are easy to maintain and are cost effective.
In this blog post, we will further extend the architecture to gather real-time feedback about the virtual trainings and create data-driven insights to measure its effectiveness with the help of Amazon artificial intelligence (AI) services.
Architecture and its benefits
Consider a scenario where you are a vendor in the health care sector. You’ve developed a cutting-edge device, such as patient vital monitoring hardware that goes through frequent software upgrades and it is about to be rolled out across different U.S. hospitals. The nursing staff needs to be well trained before it can begin using the device. Let’s take a look at an architecture to solve this problem. We will first explain the architecture for building the training and then we will show how we can measure its effectiveness.
At the core of the architecture is Amazon Sumerian. Sumerian is a managed service that lets you create and run 3D, Augmented Reality (AR), and Virtual Reality (VR) applications. Within Sumerian, real-life scenes from a hospital environment can be created by importing the assets from the assets library. Scenes consist of host(s) and an AI-driven animated character with built-in animation, speech, and behavior. The hosts act as virtual trainers that interact with the nursing staff. The speech component assigns text to the virtual trainer for playback with Amazon Polly. Polly helps convert training content from Sumerian to life-like speech in real time and ensures the nursing staff receives the latest content related to the equipment on which it’s being trained.
The nursing staff accesses the training via web browsers on iOS or Android mobile devices or laptops, and authenticates using Amazon Cognito. Cognito is a service that lets you easily add user sign-up and authentication to your mobile and web apps. Sumerian then uses the Cognito identity pool to create temporary credentials to access AWS services.
The flow of the interactions within Sumerian is controlled using a visual state machine in the Sumerian editor. Within the editor, the dialogue component assigns an Amazon Lex chatbot to an entity, in this case the virtual trainer or host. Lex is a service for building conversational interfaces with voice and text. It provides you the ability to have interactive conversations with the nursing staff, understand its areas of interest, and deliver appropriate training material. This is an important aspect of the architecture where you can customize the training per users’ needs.
Lex has native interoperability with AWS Lambda, a serverless compute offering where you just write and run your code in Lambda functions. Lambda can be used to validate user inputs or apply any business logic, such as fetching the user selected training material from Amazon DynamoDB (or another database) in real time. This material is then delivered to Lex as a response to user queries.
You can extend the state machine within the Sumerian editor to introduce new interactive flows to collect user feedback. Amazon Lex collects user feedback, which is saved in Amazon Simple Storage Service (S3) and analyzed by Amazon Comprehend. Amazon Comprehend is a natural language processing service that uses AI to find meaning and insights/sentiments in text. Insights from user feedback are stored in S3, which is a highly scalable, durable, and highly available object storage.
You can analyze the insights from user feedback using Amazon Athena, an interactive query service which analyzes data in S3 using standard SQL. You can then easily build visualizations using Amazon QuickSight.
By using this architecture, you not only deliver the virtual training to your nursing staff in an immersive environment created by Amazon Sumerian, but you can also gather the feedback interactively. You can gain insights from this feedback and iterate over it to make the training experience more effective.
Conclusion and next steps
In this blog post we reviewed the architecture to build interactive trainings and measure their effectiveness. The serverless nature of this architecture makes it cost effective, agile, and easy to manage, and you can apply it to a number of use cases. For example, an educational institution can develop training content designed for multiple learning levels and the training level can be adjusted in real time based on live interactions with the students. In the manufacturing scenario, you can build a digital twin of your process and train your resources to handle different scenarios with full interactions. You can integrate AWS services just like Lego blocks, and you can further expand this architecture to integrate with Amazon Kendra to build interactive FAQ or integrate with Amazon Comprehend Medical to build trainings for the healthcare industry. Happy building!